arXiv research

Proves new concentration inequalities for sub-gaussian and sub-exponential variables.

Researchers prove constant solutions for a specific Finslerian equation.

The purpose of this note is to describe, in terms of a power series, the distribution function of the exponential functional, taken at some independent exponential time, of a spectrally negative Lévy process ξwith unbounded variation. We also derive a Geman-Yor type formula for Asian options prices in a financial marke…

We prove that for analytic functions in low dimension, the convergence rate of the deep neural network approximation is exponential.

Kernel estimator optimally recovers function from noisy exponential Radon transform.

A new method for exponentially weighted moving models using approximations.

A new machine learning model uses matrix exponentials for universal approximation.

New energy functional and fields for Yang-Mills theory, proving monotonicity and vanishing theorems.

Exponential functionals of Brownian motion have been extensively studied in financial and insurance mathematics due to their broad applications, for example, in the pricing of Asian options. The Black-Scholes model is appealing because of mathematical tractability, yet empirical evidence shows that geometric Brownian m…

Paper solves best approximation by exponential functions for economic data.

Three-hidden-layer neural networks can approximate Hölder continuous functions uniformly with exponential rate.

This paper studies a class of exponential family models whose canonical parameters are specified as linear functionals of an unknown infinite-dimensional slope function. The optimal minimax rates of convergence for slope function estimation are established. The estimators that achieve the optimal rates are constructed …

We produce examples of groups of type F_3 with 2-dimensional Dehn functions of the form exp^n(x) (a tower of exponentials of height n), where n is any natural number.

In the paper, the author studies properties of three functions relating to the exponential function and the existence of partitions of unity, including accurate and explicit computation of their derivatives, analyticity, complete monotonicity, logarithmically complete monotonicity, absolute monotonicity, and the like.

Correspondence found between exponential families and affine Grassmannians.

We prove that the number of combinatorially distinct causal 3-dimensional triangulations homeomorphic to the 3-dimensional sphere is bounded by an exponential function of the number of tetrahedra. It is also proven that the number of combinatorially distinct causal 4-dimensional triangulations homeomorphic to the 4-sph…

We study an optimization problem for a portfolio with a risk-free, a liquid, and an illiquid risky asset. The illiquid risky asset is sold in an exogenous random moment with a prescribed liquidation time distribution. The investor prefers a negative or a positive exponential utility function. We prove that both cases a…

A function is exponentially concave if its exponential is concave. We consider exponentially concave functions on the unit simplex. In a previous paper we showed that gradient maps of exponentially concave functions provide solutions to a Monge-Kantorovich optimal transport problem and give a better gradient approximat…

The study shows exponential distortion in virtually special groups containing free subgroups.

The logistic regression model is known to converge to a Poisson point process model if the binary response tends to infinitely imbalanced. In this paper, it is shown that this phenomenon is universal in a wide class of link functions on binomial regression. The proof relies on the extreme value theory. For the logit, p…

We analyze the data on personal income distribution from the Australian Bureau of Statistics. We compare fits of the data to the exponential, log-normal, and gamma distributions. The exponential function gives a good (albeit not perfect) description of 98% of the population in the lower part of the distribution. The lo…

Paper explores connections between loss functions and consistency in binary classification and regression.

First order methods can take extremely long to find global minima of non-convex functions.

Exponential Lasso improves Lasso's robustness to outliers and heavy-tailed noise.

Incorporates matrix exponential into generative flows for improved performance.

New RL approach handles non-exponential discounting for sequential decisions.

Introduces a new theoretical framework for exponential smoothing.

Optimal insurance policy for exponential utility maximization with convex premium calculation.

Recently, self-normalizing neural networks (SNNs) have been proposed with the intention to avoid batch or weight normalization. The key step in SNNs is to properly scale the exponential linear unit (referred to as SELU) to inherently incorporate normalization based on central limit theory. SELU is a monotonically incre…

This paper studies stability of the exponential utility maximization when there are small variations on agent's utility function. Two settings are considered. First, in a general semimartingale model where random endowments are present, a sequence of utilities defined on R converges to the exponential utility. Under a …

Study on harmonic functions in spaces with collapsing behaviors.

New algorithm trains deep neural networks without global optimization.

Adapts to estimate functions from noisy ERT data.

New approach to concentration inequalities for unbounded state space dynamical systems.

Signals are generally modeled as a superposition of exponential functions in spectroscopy of chemistry, biology and medical imaging. For fast data acquisition or other inevitable reasons, however, only a small amount of samples may be acquired and thus how to recover the full signal becomes an active research topic. Bu…

Unified framework for analyzing gradient flows of measures with exponential decay of entropy.

Study dynamics of $L^p$-multipliers on harmonic manifolds with exponential volume growth.

Exponential dispersion model is a useful framework in machine learning and statistics. Primarily, thanks to the additive structure of the model, it can be achieved without difficulty to estimate parameters including mean. However, tight conditions on cumulant function, such as analyticity, strict convexity, and steepne…

Exponential rate of convergence for harmonic heat flow maps.

This work proposes the Bregman-Tweedie classification model and analyzes the domain structure of the extended exponential function, an extension of the classic generalized exponential function with additional scaling parameter, and related high-level mathematical structures, such as the Bregman-Tweedie loss function an…

Analytical solution found for a three-layer network with a specific activation function.

Consider the one-parameter generalizations of the logarithmic and exponential functions which are obtained from the integration of non-symmetrical hyperboles. These generalizations coincide to the one obtained in the context of non-extensive thermostatistics. We show that these functions are suitable to describe and un…

We study the probability distribution of stock returns at mesoscopic time lags (return horizons) ranging from about an hour to about a month. While at shorter microscopic time lags the distribution has power-law tails, for mesoscopic times the bulk of the distribution (more than 99% of the probability) follows an expon…

Abstract result on correlations of pairs in exponentially growing discrete subsets.

In this paper we study the exponential functionals of the processes $X$ with independent increments , namely $I_t= \int _0^t\exp(-X_s)ds, _,\,\, t\geq 0,$ and also $I_{\infty}= \int _0^{\infty}\exp(-X_s)ds.$ When $X$ is a semi-martingale with absolutely continuous characteristics, we derive recurrent integral equat…

This paper deals with the evaluation of double line integrals of the squared exponential covariance function. We propose a new approach in which the double integral is reduced to a single integral using the error function. This single integral is then computed with efficiently implemented numerical techniques. The perf…

Using a family of modified Weibull distributions, encompassing both sub-exponentials and super-exponentials, to parameterize the marginal distributions of asset returns and their multivariate generalizations with Gaussian copulas, we offer exact formulas for the tails of the distribution $P(S)$ of returns $S$ of a port…

This work introduces a new probabilistic process for regularization in machine learning.